Protein Structure Research Research Articles

The Role of Macrophages in Airway Disease Focusing on Porcine Reproductive and Respiratory Syndrome Virus and the Treatment with Antioxidant Nanoparticles.

Lung macrophage cells play a critical role in various lung diseases, and their state can change depending on the progression of the disease by inducing either an inflammatory or anti-inflammatory state. In this review, the potential therapeutic effects of treatment with antioxidant nanoparticles in air-borne diseases focusing on porcine reproductive and respiratory virus (PRRSV), considering reactive oxygen species (ROS) as one of the factors that regulate M1 and M2 macrophages in the inflammatory and anti-inflammatory states, respectively, was described. In addition, the author examines the status of protein structure research on CD163 (one of the markers of anti-inflammatory M2 macrophages) in human and veterinary lung diseases.

Targeting TYK2 for Fighting Diseases: Recent Advance of TYK2 Inhibitors.

TYK2 (tyrosine-protein kinase 2) is a non-receptor protein kinase belonging to the JAK family and is closely associated with various diseases, such as psoriasis, inflammatory bowel disease, systemic lupus erythematosus. TYK2 activates the downstream proteins STAT1-5 by participating in the signal transduction of immune factors such as IL-12, IL-23, and IL-10, resulting in immune expression. The activity of the inhibitor TYK2 can effectively block the transduction of excessive immune signals and treat diseases. TYK2 inhibitors are divided into two types of inhibitors according to the different binding sites. One is a TYK2 inhibitor that binds to JH2 and inhibits its activity through an allosteric mechanism. The representative inhibitor is BMS-986165, developed by Bristol-Myers Squibb. The other class binds to the JH1 adenosine triphosphate (ATP) site and prevents the catalytic activity of the kinase by blocking ATP and downstream phosphorylation. This paper mainly introduces the protein structure, signaling pathway, synthesis, structure-activity relationship and clinical research of TYK2 inhibitors.

Freeprotmap: waiting-free prediction method for protein distance map

BackgroundProtein residue–residue distance maps are used for remote homology detection, protein information estimation, and protein structure research. However, existing prediction approaches are time-consuming, and hundreds of millions of proteins are discovered each year, necessitating the development of a rapid and reliable prediction method for protein residue–residue distances. Moreover, because many proteins lack known homologous sequences, a waiting-free and alignment-free deep learning method is needed.ResultIn this study, we propose a learning framework named FreeProtMap. In terms of protein representation processing, the proposed group pooling in FreeProtMap effectively mitigates issues arising from high-dimensional sparseness in protein representation. In terms of model structure, we have made several careful designs. Firstly, it is designed based on the locality of protein structures and triangular inequality distance constraints to improve prediction accuracy. Secondly, inference speed is improved by using additive attention and lightweight design. Besides, the generalization ability is improved by using bottlenecks and a neural network block named local microformer. As a result, FreeProtMap can predict protein residue–residue distances in tens of milliseconds and has higher precision than the best structure prediction method.ConclusionSeveral groups of comparative experiments and ablation experiments verify the effectiveness of the designs. The results demonstrate that FreeProtMap significantly outperforms other state-of-the-art methods in accurate protein residue–residue distance prediction, which is beneficial for lots of protein research works. It is worth mentioning that we could scan all proteins discovered each year based on FreeProtMap to find structurally similar proteins in a short time because the fact that the structure similarity calculation method based on distance maps is much less time-consuming than algorithms based on 3D structures.

Attenphos: General Phosphorylation Site Prediction Model Based on Attention Mechanism.

Phosphorylation site prediction has important application value in the field of bioinformatics. It can act as an important reference and help with protein function research, protein structure research, and drug discovery. So, it is of great significance to propose scientific and effective calculation methods to accurately predict phosphorylation sites. In this study, we propose a new method, Attenphos, based on the self-attention mechanism for predicting general phosphorylation sites in proteins. The method not only captures the long-range dependence information of proteins but also better represents the correlation between amino acids through feature vector encoding transformation. Attenphos takes advantage of the one-dimensional convolutional layer to reduce the number of model parameters, improve model efficiency and prediction accuracy, and enhance model generalization. Comparisons between our method and existing state-of-the-art prediction tools were made using balanced datasets from human proteins and unbalanced datasets from mouse proteins. We performed prediction comparisons using independent test sets. The results showed that Attenphos demonstrated the best overall performance in the prediction of Serine (S), Threonine (T), and Tyrosine (Y) sites on both balanced and unbalanced datasets. Compared to current state-of-the-art methods, Attenphos has significantly higher prediction accuracy. This proves the potential of Attenphos in accelerating the identification and functional analysis of protein phosphorylation sites and provides new tools and ideas for biological research and drug discovery.

ML-FGAT: Identification of multi-label protein subcellular localization by interpretable graph attention networks and feature-generative adversarial networks

The prediction of multi-label protein subcellular localization (SCL) is a pivotal area in bioinformatics research. Recent advancements in protein structure research have facilitated the application of graph neural networks. This paper introduces a novel approach termed ML-FGAT. The approach begins by extracting node information of proteins from sequence data, physical-chemical properties, evolutionary insights, and structural details. Subsequently, various evolutionary techniques are integrated to consolidate multi-view information. A linear discriminant analysis framework, grounded on entropy weight, is then employed to reduce the dimensionality of the merged features. To enhance the robustness of the model, the training dataset is augmented using feature-generative adversarial networks. For the primary prediction step, graph attention networks are employed to determine multi-label protein SCL, leveraging both node and neighboring information. The interpretability is enhanced by analyzing the attention weight parameters. The training is based on the Gram-positive bacteria dataset, while validation employs newly constructed datasets: human, virus, Gram-negative bacteria, plant, and SARS-CoV-2. Following a leave-one-out cross-validation procedure, ML-FGAT demonstrates noteworthy superiority in this domain.

Quantum K-means Clustering and Classical k Means Clustering For Chest Pain Classification Using Qiskit

Abstract: Quantum computing is an upcoming field of technology which has a broad scope of increasing the current technology in a robust manner. The application area Quantum computing is very huge which ranges from battery research, protein structure research to advance computing and security areas like cryptography, quantum internet, quantum machine learning and quantum cyber security. The quantum machine learning area seems to be the most interesting because of the computing capability of a real time quantum computer. With the quantum machine learning algorithm, classical algorithm, processing speed of current quantum computers and available data in modern world we could gain insights and find patterns which were previously not possible at all.

A Study on the Relationship between the 3-D Structure of Spike Proteins and Infectiousness of SARS-CoV-2 Delta Variant

Since the outbreak of novel coronavirus pneumonia in Wuhan in 2019, the SARS-CoV-2 epidemic has become a hot topic. Over time, SARS-CoV-2 has evolved many variants. The diversity of the 3-D structure of the variant’s proteins resulted in the difference in the binding ability and infectious differences between different virus variants and human angiotensin-converting enzyme 2 (ACE2) receptors. In 2020, an evolutionary analysis of the Delta and Delta Plus variants of SARS-CoV-2 provided a three-dimensional model of the protein of the delta variant. However, it only focused on the delta variant and Delta plus variant themselves and did not compare the delta variant or delta plus variant with the original strain. It is hard to give a direct or apparent reason why the delta variant is more infectious and difficult to cure than the original strain. Therefore, this paper further compared the 3-D structures of homologous trimeric spike glycoproteins (S-proteins) and the receptor-binding domain between the SARS-COV-2 original strain and the SARS-COV-2 delta variant. By observing and analyzing the models of the above proteins in the PyMOL Molecular Graphics System, the reasons for the increase of infectivity of the delta variant can be interpreted in a direct way. This article also focuses on the data of the Indian cases from the JHU database to deeply analyze the relationship between the structure and transmission ability of the SARS-CoV-2 delta variant. Last but not least, the reproductive ability of SARS-CoV-2 can be reflected by the number of NAG (2-acetamido-2-deoxy-beta-D-glucopyranose). Through data analysis and protein structure research, we can better understand the characteristics of the binding of SARS-CoV-2 to the human receptor, thus providing a theoretical basis for accurately predicting virus variation. Through the comparative study of virus structure and infectiousness, this paper will provide a scientific basis for the relevant departments to improve epidemic prevention and improve the public's vigilance against virus variants.

The next decade of protein structure

With recent dramatic advances in various techniques used for protein structure research, we asked researchers to comment on the next exciting questions for the field and about how these techniques will advance our knowledge not only about proteins but also about human health and diseases.

Protein Subcellular Localization Prediction Model Based on Graph Convolutional Network

Protein subcellular localization prediction is an important research area in bioinformatics, which plays an essential role in understanding protein function and mechanism. Many machine learning and deep learning algorithms have been employed for this task, but most of them do not use structural information of proteins. With the advances in protein structure research in recent years, protein contact map prediction has been dramatically enhanced. In this paper, we present GraphLoc, a deep learning model that predicts the localization of proteins at the subcellular level. The cores of the model are a graph convolutional neural network module and a multi-head attention module. The protein topology graph is constructed based on a contact map predicted from protein sequences, which is used as the input of the GCN module to take full advantage of the structural information of proteins. Multi-head attention module learns the weighted contribution of different amino acids to subcellular localization in different feature representation subspaces. Experiments on the benchmark dataset show that the performance of our model is better than others. The code can be accessed at https://github.com/GoodGuy398/GraphLoc . The proposed GraphLoc model consists of three parts. The first part is a graph convolutional network (GCN) module, which utilizes the predicted contact maps to construct protein graph, taking benefit of protein information accordingly. The second part is the multi-head attention module, which learns the weighted contribution of different amino acids in different feature representation subspace, and weighted average the feature map across all amino acid nodes. The last part is a fully connected layer that maps the flatten graph representation vector to another vector with a category number dimension, followed by a softmax layer to predict the protein subcellular localization.

An End-to-End Deep Learning Pipeline for Assigning Secondary Structure in Proteins

Protein secondary structure assignment, a subdiscipline of computational chemistry, is yet to be explored using deep learning techniques. Protein secondary structure elements are assigned to support structural analysis and prediction. Algorithms like DSSP, generally regarded as the gold standard for assigning the secondary structure of proteins, need full atom information to label protein coordinates. The PDB database has been the major repository for data on the 3D structures of proteins, nucleic acids, and other complex assemblies since 1971. However, a significant fraction of protein structures contains missing atoms. As a result, new approaches to reliably and consistently assigning secondary structures based on coarse-grained atomic coordinates are needed. While deep learning architectures have an unparalleled track record in applications such as protein structure prediction, there are only a few known deep learning solutions for structure assignment problems. While the gold standard methods are based on bonding information and other geometric characteristics, deep learning methods extract features themselves without human intervention. The benefit that standardised datasets provide to the effectiveness of deep learning systems in multiple domains motivated us to create a labeled dataset for protein structure assignment tasks. Additionally, a deep learning model, named PSSADL, solely based on [Formula: see text] coordinates was trained on the generated dataset to validate its potency. The proposed method, which combines Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM)/Bidirectional LSTM networks, has been compared to the established standards and more recent techniques. The model achieved an accuracy of [Formula: see text] on the benchmark and individual test sets. The results show that deep learning techniques have a promising future in protein structure analysis, implying that the dataset developed as part of our work will be a valuable resource for further protein structure research.

The effects of thermal treatments on the antigenicity and structural properties of soybean glycinin.

The effects of thermal treatments on the antigenicity and structural properties of soybean glycinin were investigated. An indirect enzyme-linked immunosorbent assay (iELISA) result indicated that the antigenicity of the soybean glycinin decreased to the lowest level when the heat treatment time was 50min and the temperature was set at 110°C. The reducing sodium dodecyl sulfate-Gel electrophoresis results showed that the heat treatment had promoted the formation of small molecular weight protein subunits and concentrated protein. The free sulfhydryl (-SH) group of glycinin had increased significantly (p<.05) under the conditions of temperature between 90 and 120°C and 40 to 50min heat treatments. The maximum fluorescence wavelength of the intrinsic fluorescence also changed significantly when compared with the control. The circular dichroism showed that the number of disordered structures had also increased significantly. These results provided evidence that the heat-induced structural modifications of glycinin will alter the antigenicity of soybean glycinin. PRACTICAL APPLICATIONS: The relationship between antigenicity and structural properties of heat-treated soybean globulin was studied by different protein structural research methods. The results showed that the antigenicity of the protein was minimized at 110°C 50min, which could be used in food safety production. This study chose to analyze the changes in the antigenicity and structural characteristics of soybean glycinin, both prior to and after heat treatments, for the purpose of promoting the development of low allergenic food products.

Allostery: The Rebound of Proteins.

The discovery of hemoglobin allosteric properties is briefly summarized and contextualized in the frame of the main biochemical revelations that characterized the first half of the XX century. In particular, the historical background of DNA, RNA, and protein structure research is recalled and the new role that protein-protein interaction may have on allosteric regulation is discussed.

Optimized inverse PCR strategy for constructing multilocus mutants efficiently

Mutants of proteins are the basis for studying their structure and function, this work aimed to establish an efficient and rapid method for constructing multi-site mutants. When four or more adjacent amino acid residues need to be mutated, firstly, two long and two short primers (long primers Ⅰ/Ⅰ, short primersⅡ/Ⅱ) were designed: the long primers contain mutated sites, and the number of mutant bases is ≤20 bp, the short primers do not contain mutated sites; GC contents of the long and short primers are ≤80%, and the difference of annealing temperature is ≤40 °C. Then two sets of reverse PCR amplifications were performed using primer pairs (Ⅰ/Ⅱand Ⅰ/Ⅱ) and templates, respectively. After amplification, each system can obtain non-methylated linear plasmids which contain mutated sites, and the breakpoints of the two sets of linear plasmids amplified by primers Ⅰ/Ⅱ and Ⅲ/Ⅳ were distributed on both sides of the mutated sites. Followed by digested by DpnⅠ to remove the methylated templates, the recovered PCR products, which were mixed in an equimolar ratio, were performed another round of denaturation and annealing: the two sets of linear plasmids were denatured at 95 °C and then annealed with each other's single-stranded DNA as templates to form open-loop plasmids, and then the transformants containing the mutations will be obtained after transformed the open-loop plasmids into Escherichia coli competent cells. Results showed that, this method can mutate 4 to 11 consecutive amino acid residues (8-20 bp) simultaneously, which will greatly simplify the construction of multi-site mutants, Thereby improve the efficiency of protein structure and function research further.

Isoindoline-Based Nitroxides as Bioresistant Spin Labels for Protein Labeling through Cysteines and Alkyne-Bearing Noncanonical Amino Acids.

Electron paramagnetic resonance (EPR) spectroscopy in combination with site‐directed spin labeling (SDSL) is a powerful tool in protein structural research. Nitroxides are highly suitable spin labeling reagents, but suffer from limited stability, particularly in the cellular environment. Herein we present the synthesis of a maleimide‐ and an azide‐modified tetraethyl‐shielded isoindoline‐based nitroxide (M‐ and Az‐TEIO) for labeling of cysteines or the noncanonical amino acid para‐ethynyl‐l‐phenylalanine (pENF). We demonstrate the high stability of TEIO site‐specifically attached to the protein thioredoxin (TRX) against reduction in prokaryotic and eukaryotic environments, and conduct double electron–electron resonance (DEER) measurements. We further generate a rotamer library for the new residue pENF‐Az‐TEIO that affords a distance distribution that is in agreement with the measured distribution.

Double Nitroxide Labeling by Copper-Catalyzed Azide-Alkyne Cycloadditions with Noncanonical Amino Acids for Electron Paramagnetic Resonance Spectroscopy.

Electron paramagnetic resonance spectroscopy in combination with site-directed spin labeling (SDSL) is an important tool to obtain long-range distance restraints for protein structural research. We here study a variety of azide- and alkyne-bearing noncanonical amino acids (ncAA) in terms of protein single- and double-incorporation efficiency via nonsense suppression, metabolic stability, yields of nitroxide labeling via copper-catalyzed [3 + 2] azide–alkyne cycloadditions (CuAAC), and spectroscopic properties in continuous-wave and double electron–electron resonance measurements. We identify para-ethynyl-l-phenylalanine and para-propargyloxy-l-phenylalanine as suitable ncAA for CuAAC-based SDSL that will complement current SDSL approaches, particularly in cases in which essential cysteines of a target protein prevent the use of sulfhydryl-reactive spin labels.

Protein Inter-Residue Contacts Prediction: Methods, Performances and Applications

Background:Protein inter-residue contacts prediction play an important role in the field of protein structure and function research. As a low-dimensional representation of protein tertiary structure, protein inter-residue contacts could greatly help de novo protein structure prediction methods to reduce the conformational search space. Over the past two decades, various methods have been developed for protein inter-residue contacts prediction.Objective:We provide a comprehensive and systematic review of protein inter-residue contacts prediction methods.Results:Protein inter-residue contacts prediction methods are roughly classified into five categories: correlated mutations methods, machine-learning methods, fusion methods, templatebased methods and 3D model-based methods. In this paper, firstly we describe the common definition of protein inter-residue contacts and show the typical application of protein inter-residue contacts. Then, we present a comprehensive review of the three main categories for protein interresidue contacts prediction: correlated mutations methods, machine-learning methods and fusion methods. Besides, we analyze the constraints for each category. Furthermore, we compare several representative methods on the CASP11 dataset and discuss performances of these methods in detail.Conclusion:Correlated mutations methods achieve better performances for long-range contacts, while the machine-learning method performs well for short-range contacts. Fusion methods could take advantage of the machine-learning and correlated mutations methods. Employing more effective fusion strategy could be helpful to further improve the performances of fusion methods.

PubMed Text Similarity Model and its application to curation efforts in the Conserved Domain Database.

This study proposes a text similarity model to help biocuration efforts of the Conserved Domain Database (CDD). CDD is a curated resource that catalogs annotated multiple sequence alignment models for ancient domains and full-length proteins. These models allow for fast searching and quick identification of conserved motifs in protein sequences via Reverse PSI-BLAST. In addition, CDD curators prepare summaries detailing the function of these conserved domains and specific protein families, based on published peer-reviewed articles. To facilitate information access for database users, it is desirable to specifically identify the referenced articles that support the assertions of curator-composed sentences. Moreover, CDD curators desire an alert system that scans the newly published literature and proposes related articles of relevance to the existing CDD records. Our approach to address these needs is a text similarity method that automatically maps a curator-written statement to candidate sentences extracted from the list of referenced articles, as well as the articles in the PubMed Central database. To evaluate this proposal, we paired CDD description sentences with the top 10 matching sentences from the literature, which were given to curators for review. Through this exercise, we discovered that we were able to map the articles in the reference list to the CDD description statements with an accuracy of 77%. In the dataset that was reviewed by curators, we were able to successfully provide references for 86% of the curator statements. In addition, we suggested new articles for curator review, which were accepted by curators to be added into the reference list at an acceptance rate of 50%. Through this process, we developed a substantial corpus of similar sentences from biomedical articles on protein sequence, structure and function research, which constitute the CDD text similarity corpus. This corpus contains 5159 sentence pairs judged for their similarity on a scale from 1 (low) to 5 (high) doubly annotated by four CDD curators. Curator-assigned similarity scores have a Pearson correlation coefficient of 0.70 and an inter-annotator agreement of 85%. To date, this is the largest biomedical text similarity resource that has been manually judged, evaluated and made publicly available to the community to foster research and development of text similarity algorithms.

Using Grey Model to Predict Protein Remote Homologous Family

Protein remote homology detection is the most basic and core problems of protein structure and function research. The purpose of protein remote homology detection to detect the remote evolutionary relationship between proteins by computation methods. At present, there are many methods for remote homology detection of proteins, but some methods are not very effective. In bioinformatics, it is urgent to further improve the performance of protein remote homology detection. In this study, we propose a new model called PHom-GRA to detect protein remote homology, which is the integration of various ranking methods via using grey relational analysis. Our experiment constructs benchmark dataset with lower homology, in which any two proteins have not more than 40% identity or homology. We achieve an ROC1 score of 0.7372 and an ROC50 score of 0.7968 in jack-knife test.

PINE-SPARKY.2 for automated NMR-based protein structure research.

SummaryNuclear magnetic resonance (NMR) spectroscopy, along with X-ray crystallography and cryoelectron microscopy, is one of the three major tools that enable the determination of atomic-level structural models of biological macromolecules. Of these, NMR has the unique ability to follow important processes in solution, including conformational changes, internal dynamics and protein–ligand interactions. As a means for facilitating the handling and analysis of spectra involved in these types of NMR studies, we have developed PINE-SPARKY.2, a software package that integrates and automates discrete tasks that previously required interaction with separate software packages. The graphical user interface of PINE-SPARKY.2 simplifies chemical shift assignment and verification, automated detection of secondary structural elements, predictions of flexibility and hydrophobic cores, and calculation of three-dimensional structural models.Availability and implementation PINE-SPARKY.2 is available in the latest version of NMRFAM-SPARKY from the National Magnetic Resonance Facility at Madison (http://pine.nmrfam.wisc.edu/download_packages.html), the NMRbox Project (https://nmrbox.org) and to subscribers to the SBGrid (https://sbgrid.org). For a detailed description of the program, see http://www.nmrfam.wisc.edu/pine-sparky2.htm.Supplementary information Supplementary data are available at Bioinformatics online.

Site specific labeling of two proteins in one system by atypical split inteins

Atypical inteins have been used for protein site-specific labeling and modification. S1 or S11 intein contain a small N-intein or C-intein which can be chemically synthesized and added with desired labels or modifications. However, seldom reports have been found to develop multi-protein specific labeling in one system at the same time. Here, we established a specific labeling method for two proteins based on three pairs of atypical inteins, RBS1 (Rma DnaB S1)/SGS1 (Ssp GyrB S1) intein, TE3S11 (Ter DnaE-3 S11)/SGS11 (Ssp GyrB S11) intein, and RBS1/TE3S11 intein. Firstly, intein fragments were respectively fused with model proteins, expressed and purified as precursors. The non-cross reactivity between inteins was then determined by splicing reactions analysis through Western-blot. Finally, the model proteins were specifically labeled with fluorescent groups in one system mediating these intein pairs, and can be labeled even in the cell lysate. Our results for the first time report a method labeling the N/C-terminal of two proteins altogether in the same system based on above intein pairs with a marked splicing efficiency, which could potentially be used for protein structural and functional research or some specific applications.